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Record W2012580469 · doi:10.1063/1.4745027

A transient electron transport analysis of bulk wurtzite zinc oxide

2012· article· en· W2012580469 on OpenAlexaff
Walid A. Hadi, M. S. Shur, Stephen K. O’Leary

Bibliographic record

VenueJournal of Applied Physics · 2012
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Windsor
Fundersnot available
KeywordsDrift velocityElectric fieldWurtzite crystal structureGallium nitrideElectronSaturation velocityElectron mobilityWide-bandgap semiconductorVelocity overshootAtomic physicsMaterials scienceVelocity saturationCondensed matter physicsChemistryZincOptoelectronicsPhysicsMOSFETNanotechnology

Abstract

fetched live from OpenAlex

A three-valley Monte Carlo simulation approach is used in order to probe the transient electron transport that occurs within bulk wurtzite zinc oxide. For the purposes of this analysis, we follow O’Leary et al. [Solid State Commun. 150, 2182 (2010)], and study how electrons, initially in thermal equilibrium, respond to the sudden application of a constant applied electric field. We find that for applied electric field strength selections in excess of 300 kV/cm that an overshoot in the electron drift velocity is observed. An undershoot in the electron drift velocity is also observed for applied electric field strength selections in excess of 700 kV/cm, this velocity undershoot not being observed for other compound semiconductors, such as gallium arsenide and gallium nitride. We employ a means of rendering transparent the electron drift velocity enhancement offered by the transient electron transport, and then use the calculated dependence of the peak transient electron drift velocity on the applied electric field for the design optimization of short-channel high-frequency electron devices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.237
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2012
Admission routes1
Has abstractyes

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